Integration of Mechanical and Chemical Signals in Cell Migration

Integration of Mechanical and Chemical Signals in Cell Migration

Abstract:

Cell migration is driven by the self-organization of innumerable chemical and mechanical processes. Chemical signals activate and deactivate the assembly and disassembly of cytoskeleton polymer networks. Both assembly and disassembly generate mechanical forces that push parts of the cell forward and retract others. They also define the mechanical properties of the cytoskeleton, which in turn determine how forces translate into cell shape deformation and movements. Chemical signals modulate the activity of molecular motors that produce contractile forces. By pulling on the polymer networks, these same motors organize the architecture of the cytoskeleton. This affects again the mechanical properties of the cytoskeleton. Motors may even mediate polymer disassembly, inducing a secondary contractile response of the cytoskeleton. All these entangled mechanical outputs feed back into the activation of the upstream chemical signals, a process that can be referred to as cell intrinsic mechanotransduction. Decades of biochemical, genetic and molecular analyses have generated the parts lists for most of these chemical and mechanical processes as well as hypotheses of process interactions. However, there is still very limited understanding as to how the processes are spatially and temporally organized in a system with emergent properties. There are multiple challenges associated with addressing this question: First, the process hierarchies are transient and distributed over multiple time and length scales. Thus, new technology is required to monitor multiple processes working in parallel but at different time points and cellular locations; and analytical tools are needed to extract from these data the spatial, temporal, and functional linkages between processes. Second, because of the nested nonlinear interactions among processes, the stimulation or perturbation of a particular process component often propagates in complex ways, obscuring the relation between system response and component function. For the past ten years my lab has made attempts to implement approaches that tackle these issues. We have invested in the development of quantitative multi-parametric live cell imaging to acquire simultaneous measurements of cell morphodynamics, cytoskeleton dynamics, resulting intracellular forces, and chemical signals. We also have worked on multiplexing methods to couple these measurements across different experiments, allowing us to “stitch together” larger process models. Very recent work in the lab has focused on novel mathematical tools to predict complex nonlinear process hierarchies. Central to our approach is the use of minimally perturbing experimental strategies that avoid the ambiguities of nonlinear system responses induced by stronger interventions. This talk will highlight some of the most surprising discoveries we have made with this work and will give an outlook to the many challenges we are still facing in order to establish a comprehensive understanding of cell migration.